Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices

This paper introduces a novel probabilistic forecasting technique called Smoothing Quantile Regression Averaging (SQRA). It combines Quantile Regression Averaging - a well performing load and price forecasting approach - with kernel estimation to improve the reliability of the estimates. Three variants of SQRA are evaluated across datasets from four power markets and compared against well-established benchmarks. Empirical evidence indicates superior predictive performance of the method in terms of the Kupiec test, the pinball score, and the conditional predictive accuracy test. Moreover, considering a day-ahead market trading strategy that utilizes probabilistic price predictions and battery storage, the study shows that profits of up to 9 EUR per 1 MW traded can be achieved when forecasts are generated using SQRA.